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Perfect Density Models Cannot Guarantee Anomaly Detection

by 1,2,* and 2
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
Google Research, Montreal, QC H3B 2Y5, Canada
Author to whom correspondence should be addressed.
Academic Editors: Eric Nalisnick and Boris Ryabko
Entropy 2021, 23(12), 1690;
Received: 30 September 2021 / Revised: 3 November 2021 / Accepted: 13 December 2021 / Published: 16 December 2021
(This article belongs to the Special Issue Probabilistic Methods for Deep Learning)
Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection. View Full-Text
Keywords: deep generative modeling; probabilistic modeling; anomaly detection deep generative modeling; probabilistic modeling; anomaly detection
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MDPI and ACS Style

Le Lan, C.; Dinh, L. Perfect Density Models Cannot Guarantee Anomaly Detection. Entropy 2021, 23, 1690.

AMA Style

Le Lan C, Dinh L. Perfect Density Models Cannot Guarantee Anomaly Detection. Entropy. 2021; 23(12):1690.

Chicago/Turabian Style

Le Lan, Charline, and Laurent Dinh. 2021. "Perfect Density Models Cannot Guarantee Anomaly Detection" Entropy 23, no. 12: 1690.

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